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Efficiency in cryptocurrency markets: new evidence

Author

Listed:
  • Carmen López-Martín

    (National Distance Education University (UNED))

  • Sonia Benito Muela

    (National Distance Education University (UNED))

  • Raquel Arguedas

    (National Distance Education University (UNED))

Abstract

In this paper we carried out a comprehensive study of the efficiency in the cryptocurrency markets. The markets under study are: Bitcoin, Litecoin, Ethereum, Ripple, Stellar and Monero. To study the efficiency of these markets, we use a set of five test which are applied in both a static context and dynamic context. The results obtained depend on both the analysis period and the methodology used to test the predictability of the return. However, some conclusions can be drawn: first, we observe that overall, the efficiency degree tends to increase with the time. Second, although the efficiency market seems to change along the period, the changes in the Bitcoin, Litecoin and Ethereum market show a clear tendency that evolves from less to more efficiency. In the case of Ripple, Stellar and Monero, periods of efficiency alternate with periods of inefficient, which is consistent with the adaptive market hypothesis.

Suggested Citation

  • Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    • Handle: RePEc:spr:eurase:v:11:y:2021:i:3:d:10.1007_s40822-021-00182-5
    DOI: 10.1007/s40822-021-00182-5
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    2. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    3. Zhuhua Jiang & Walid Mensi & Seong-Min Yoon, 2023. "Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    4. Burke, Matt & Fry, John & Kemp, Sean & Woodhouse, Drew, 2022. "Attention to Authority: The behavioural finance of Covid-19," Finance Research Letters, Elsevier, vol. 49(C).
    5. Marianna Brunetti & Roberta De Luca, 2023. "Pairs trading in the index options market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 145-173, March.
    6. Tomas Pečiulis & Nisar Ahmad & Angeliki N. Menegaki & Aqsa Bibi, 2024. "Forecasting of cryptocurrencies: Mapping trends, influential sources, and research themes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1880-1901, September.
    7. López-Martín, Carmen & Arguedas-Sanz, Raquel & Muela, Sonia Benito, 2022. "A cryptocurrency empirical study focused on evaluating their distribution functions," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 387-407.
    8. Zhang, Zehua & Zhao, Ran, 2023. "Good volatility, bad volatility, and the cross section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 89(C).
    9. Mustafa Tevfik Kartal & Mustafa Kevser & Fatih Ayhan, 2023. "Asymmetric effects of global factors on return of cryptocurrencies by novel nonlinear quantile approaches," Economic Change and Restructuring, Springer, vol. 56(3), pages 1515-1535, June.
    10. Mingbo Zheng & Gen-Fu Feng & Xinxin Zhao & Chun-Ping Chang, 2023. "The transaction behavior of cryptocurrency and electricity consumption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    11. Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.
    12. Jessica Morales Herrera & Ra'ul Salgado-Garc'ia, 2023. "Trend patterns statistics for assessing irreversibility in cryptocurrencies: time-asymmetry versus inefficiency," Papers 2307.08612, arXiv.org.
    13. Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.

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    More about this item

    Keywords

    Market efficiency; Adaptive market hypothesis; Cryptocurrencies; Random walk; Hurst exponent; Variance ratio test;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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